ByteNet

Digital infrastructure is in the background of almost every critical system in a world of hyper-connectivity, autonomous vehicles and medical equipment to cloud data centers and smart cities.

Yet, conventional network monitoring is still essentially reactive in nature, and many of them involve manual log inspection and threshold-triggered alerts, which do not kick in until a performance breakdown has already happened. This reactive approach results in unnoticed degradation and long downtimes that have a devastating economic impact with the average cost of service disruption amounting to about 686,000 per hour. As 93 percent of the companies that experience significant network failures file for bankruptcy within one year, the industry desperately lacks a paradigm shift in terms of adopting proactive management of systems.

Our research concentrates on this severe weakness, suggesting a solution named BytesNET; an artificial intelligence-based system, which is aimed to predict when the network performance decline and prevent an outage. Using an enhanced machine-learning-based model, i.e. XGBoost framework, with a 93.06% accuracy rate on performance comparison, BytesNET processes such high-level telecommunication KPIs as radio network availability, call drop rates, and throughput indicators to uncover concealed instability trends. The system does not just predict but creates severity levels and automated root cause analysis, which will inform the administrators on informed proactive decision-making. This work prototyping the next generation of smarter networks, by integrating SMS and email alerting features, converts volatile syndrome data into solid computation, which guarantees zero-surprise connectivity.